Approaches to knowledge reduction based on variable precision rough set model

By Jusheng Mi, Weizhi Wu, Wenxiu Zhang. Information Sciences, 2004

389 citations recorded by the source corpus.

Subfields: Artificial Intelligence, Computational Mechanics, Computational Theory and Mathematics

View Paper: https://www.semanticscholar.org/paper/008a41fe1637c624faec50cec2f8d35ccff0a8c8

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Artificial Intelligence

1. Yan Zhao
2. Yiyu Yao
3. Qinghua Hu
4. Daren Yu
5. Jiye Liang
6. Y. Qian
7. Zongxia Xie
8. Chuangyin Dang

In Computational Mechanics

1. Yiyu Yao
2. Yan Zhao
3. Qinghua Hu
4. Duoqian Miao
5. Daren Yu
6. Yuming Chen
7. Ruizhi Wang
8. Zongxia Xie

In Computational Theory and Mathematics

1. Qinghua Hu
2. Daren Yu
3. Yiyu Yao
4. Yan Zhao
5. Y. Qian
6. Zongxia Xie
7. Jiye Liang
8. Chuangyin Dang

Citations in the corpus

• Information entropy, rough entropy and knowledge granulation in incomplete information systems (2006, background): “2001, Leung and Li 2003, Mi et al. 2004). The use of the indiscernibility relation results in information granulation. The entropy of a system, as defined by Shannon (1948) gives a measure of uncertainty about its actual structure.”
• MGRS: A multi-granulation rough set (2010, background): “Intuitively, some attributes are not significant in a representation and their removal has no real impact on the value of the representation of elements [ 15 ,41,42].”
• A rough set approach to feature selection based on ant colony optimization (2010, methodology): “It has been widely applied in many fields such as machine learning (Swiniarski and Skowron, 2003), data mining (Duan et al., 2007), etc. (Mi et al., 2004).”
• Granular Computing and Knowledge Reduction in Formal Contexts (2009, background): “Many types of attribute reduction have been proposed in the analysis of information systems and decision tables [5], [15], [22], [24], [ 33 ], [38], [53], [54], [64], [67], [68], each of”
• Information-preserving hybrid data reduction based on fuzzy-rough techniques (2006, methodology): “Keywords: Attribute reduction; Hybrid data; Fuzzy-rough set; Information measure”
• Attribute reduction in decision-theoretic rough set models (2008, methodology): “The proposals include probabilistic rough set models [12,20,28,40,44,45,53,54], decision-theoretic rough set models [43,47,48], variable precision rough set models [21,52], rough membership functions [25], parameterized rough set models [27,30,34], and Bayesian rough set models [8,9,32,33].”